{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四步：调整树的参数：subsample 和 colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练数据\n",
    "train = pd.read_csv(\"RentListingInquries_FE_train.csv\")\n",
    "# 取出特征与标签\n",
    "y_train = train[\"interest_level\"].values\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': [0.6, 0.7, 0.8, 0.9],\n",
       " 'subsample': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按0.1的步长进行调整\n",
    "subsample = [i/10.0 for i in range(3,9)]\n",
    "colsample_bytree = [i/10.0 for i in range(6,10)]\n",
    "param_test4_1 = dict(subsample=subsample, colsample_bytree=colsample_bytree)\n",
    "param_test4_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[23:06:45] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:46] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:46] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:47] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:48] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:49] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:49] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:50] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:51] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 44 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:51] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:52] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:53] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:54] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:55] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:55] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[23:06:56] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.58878, std: 0.00359, params: {'subsample': 0.3, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58651, std: 0.00432, params: {'subsample': 0.4, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58500, std: 0.00323, params: {'subsample': 0.5, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58429, std: 0.00336, params: {'subsample': 0.6, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58386, std: 0.00347, params: {'subsample': 0.7, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58278, std: 0.00299, params: {'subsample': 0.8, 'colsample_bytree': 0.6},\n",
       "  mean: -0.58770, std: 0.00377, params: {'subsample': 0.3, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58613, std: 0.00433, params: {'subsample': 0.4, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58461, std: 0.00416, params: {'subsample': 0.5, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58398, std: 0.00421, params: {'subsample': 0.6, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58319, std: 0.00420, params: {'subsample': 0.7, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58314, std: 0.00417, params: {'subsample': 0.8, 'colsample_bytree': 0.7},\n",
       "  mean: -0.58813, std: 0.00425, params: {'subsample': 0.3, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58677, std: 0.00483, params: {'subsample': 0.4, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58526, std: 0.00397, params: {'subsample': 0.5, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58419, std: 0.00361, params: {'subsample': 0.6, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58343, std: 0.00351, params: {'subsample': 0.7, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58283, std: 0.00337, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: -0.58667, std: 0.00463, params: {'subsample': 0.3, 'colsample_bytree': 0.9},\n",
       "  mean: -0.58581, std: 0.00385, params: {'subsample': 0.4, 'colsample_bytree': 0.9},\n",
       "  mean: -0.58432, std: 0.00331, params: {'subsample': 0.5, 'colsample_bytree': 0.9},\n",
       "  mean: -0.58342, std: 0.00387, params: {'subsample': 0.6, 'colsample_bytree': 0.9},\n",
       "  mean: -0.58246, std: 0.00361, params: {'subsample': 0.7, 'colsample_bytree': 0.9},\n",
       "  mean: -0.58243, std: 0.00392, params: {'subsample': 0.8, 'colsample_bytree': 0.9}],\n",
       " {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       " -0.5824290207448407)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb4_1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=262,  #第三步调整得到的新n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=1, #第二步调整得到的最小叶子节点权重和和最大深度\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3,\n",
    "        silent=0)\n",
    "\n",
    "\n",
    "gsearch4_1 = GridSearchCV(xgb4_1, param_grid = param_test4_1, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch4_1.fit(X_train , y_train)\n",
    "\n",
    "gsearch4_1.grid_scores_, gsearch4_1.best_params_,     gsearch4_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 852.93300004, 1081.95959997, 1119.84140005, 1123.19560003,\n",
       "        1121.61439996, 1128.87559996, 1081.64320002, 1127.45819998,\n",
       "        1159.74759998, 1168.63040009, 1167.75560002, 1178.748     ,\n",
       "        1110.3632    , 1174.30500002, 1231.61399999, 1346.17260003,\n",
       "        1380.39960003, 1401.60459995, 1326.65280004, 1382.99300003,\n",
       "        1428.53779998, 1428.16639996, 1426.04499998, 1276.10119991]),\n",
       " 'mean_score_time': array([10.72520008,  8.12800002, 10.63439999,  8.98700004, 11.97060008,\n",
       "         9.09679999, 13.36859999, 17.37860003, 13.30520005,  8.45300002,\n",
       "         9.6302    , 15.10460005, 17.32640004, 15.13320003, 12.03500009,\n",
       "        11.34999995, 14.48319998, 15.28220005, 18.81559992, 14.67359996,\n",
       "        12.92600002, 12.33980002, 16.46540008,  5.62280006]),\n",
       " 'mean_test_score': array([-0.58878259, -0.58650833, -0.58499937, -0.58428893, -0.58385925,\n",
       "        -0.58278082, -0.58770249, -0.58612933, -0.58460829, -0.58398223,\n",
       "        -0.58319135, -0.58314445, -0.58813333, -0.5867691 , -0.58525579,\n",
       "        -0.5841917 , -0.58343462, -0.58282972, -0.58666749, -0.5858093 ,\n",
       "        -0.58432434, -0.58341559, -0.58246016, -0.58242902]),\n",
       " 'mean_train_score': array([-0.50553406, -0.5028491 , -0.50074769, -0.49972338, -0.49909561,\n",
       "        -0.50001442, -0.50229849, -0.49861207, -0.49746904, -0.49683435,\n",
       "        -0.49743448, -0.497597  , -0.5001661 , -0.49657907, -0.49451702,\n",
       "        -0.4940753 , -0.49420812, -0.49509694, -0.49768557, -0.49431943,\n",
       "        -0.49302407, -0.49163259, -0.49199942, -0.49357925]),\n",
       " 'param_colsample_bytree': masked_array(data=[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7, 0.7, 0.7,\n",
       "                    0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.9,\n",
       "                    0.9, 0.9],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_subsample': masked_array(data=[0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.3, 0.4, 0.5, 0.6, 0.7,\n",
       "                    0.8, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.3, 0.4, 0.5, 0.6,\n",
       "                    0.7, 0.8],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'colsample_bytree': 0.6, 'subsample': 0.3},\n",
       "  {'colsample_bytree': 0.6, 'subsample': 0.4},\n",
       "  {'colsample_bytree': 0.6, 'subsample': 0.5},\n",
       "  {'colsample_bytree': 0.6, 'subsample': 0.6},\n",
       "  {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
       "  {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.3},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.4},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.5},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
       "  {'colsample_bytree': 0.7, 'subsample': 0.8},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.3},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.4},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.5},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
       "  {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.3},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.4},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.5},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
       "  {'colsample_bytree': 0.9, 'subsample': 0.8}],\n",
       " 'rank_test_score': array([24, 19, 15, 12,  9,  3, 22, 18, 14, 10,  6,  5, 23, 21, 16, 11,  8,\n",
       "         4, 20, 17, 13,  7,  2,  1]),\n",
       " 'split0_test_score': array([-0.5836382 , -0.57996121, -0.58052983, -0.57867823, -0.57757089,\n",
       "        -0.57774977, -0.58182445, -0.57892704, -0.57814492, -0.57731161,\n",
       "        -0.57617069, -0.57568122, -0.58029482, -0.5788778 , -0.57841197,\n",
       "        -0.57817531, -0.57833385, -0.57707657, -0.57867819, -0.57927349,\n",
       "        -0.57842606, -0.57692736, -0.57716541, -0.57666713]),\n",
       " 'split0_train_score': array([-0.50611531, -0.50433073, -0.50200597, -0.50155358, -0.49992369,\n",
       "        -0.50129939, -0.5032443 , -0.50056942, -0.49846456, -0.4974615 ,\n",
       "        -0.49810443, -0.49771886, -0.50114067, -0.49827915, -0.49507441,\n",
       "        -0.49529321, -0.49592713, -0.49638122, -0.49955248, -0.49581073,\n",
       "        -0.49428072, -0.49150787, -0.49237173, -0.49342063]),\n",
       " 'split1_test_score': array([-0.58664048, -0.58382015, -0.58203587, -0.58264864, -0.58286765,\n",
       "        -0.58134694, -0.58521724, -0.58380246, -0.58314834, -0.58210978,\n",
       "        -0.58118297, -0.58218805, -0.58723939, -0.58495218, -0.58393872,\n",
       "        -0.58260662, -0.58105051, -0.58142576, -0.58506205, -0.58366153,\n",
       "        -0.58321454, -0.58303062, -0.5804031 , -0.5804334 ]),\n",
       " 'split1_train_score': array([-0.50608324, -0.50274187, -0.50128864, -0.50085632, -0.49972814,\n",
       "        -0.49949818, -0.50273892, -0.4992484 , -0.49836191, -0.49537844,\n",
       "        -0.49681342, -0.49727977, -0.50080269, -0.49719009, -0.49482029,\n",
       "        -0.49395397, -0.49444529, -0.49562985, -0.49856158, -0.49435157,\n",
       "        -0.49468375, -0.49384101, -0.4913352 , -0.49510347]),\n",
       " 'split2_test_score': array([-0.58855887, -0.58660768, -0.58579486, -0.58509107, -0.58513359,\n",
       "        -0.58342841, -0.58896682, -0.58753434, -0.58368309, -0.58416675,\n",
       "        -0.58410648, -0.58395477, -0.58989426, -0.58725223, -0.58596869,\n",
       "        -0.58474671, -0.58322712, -0.58363402, -0.58729988, -0.58807161,\n",
       "        -0.58553839, -0.58237666, -0.58180112, -0.58142171]),\n",
       " 'split2_train_score': array([-0.50554559, -0.50238484, -0.50005818, -0.49899609, -0.49873816,\n",
       "        -0.50056929, -0.5019354 , -0.49922366, -0.49725715, -0.49702341,\n",
       "        -0.49717767, -0.49777805, -0.49978872, -0.49636176, -0.49460167,\n",
       "        -0.49380269, -0.49397224, -0.49386299, -0.49677085, -0.49462942,\n",
       "        -0.49227386, -0.49236208, -0.49251678, -0.49322259]),\n",
       " 'split3_test_score': array([-0.59093786, -0.59193082, -0.58777143, -0.58810401, -0.58684253,\n",
       "        -0.58576021, -0.58995801, -0.58927892, -0.58791293, -0.58648549,\n",
       "        -0.58647256, -0.58632004, -0.59114031, -0.58936178, -0.5879748 ,\n",
       "        -0.58688616, -0.58705431, -0.58535418, -0.59118306, -0.58976578,\n",
       "        -0.58679482, -0.58769075, -0.58680086, -0.58669109]),\n",
       " 'split3_train_score': array([-0.50486605, -0.50383458, -0.49945345, -0.49807873, -0.49926291,\n",
       "        -0.50002755, -0.50273078, -0.49807777, -0.49683474, -0.49714688,\n",
       "        -0.49740671, -0.49737366, -0.50087883, -0.49596609, -0.49470644,\n",
       "        -0.49430591, -0.49385682, -0.49414739, -0.49703825, -0.49272298,\n",
       "        -0.49113931, -0.48990858, -0.49269704, -0.49317755]),\n",
       " 'split4_test_score': array([-0.59413917, -0.5902229 , -0.58886605, -0.58692352, -0.58688251,\n",
       "        -0.58561965, -0.5925474 , -0.5911054 , -0.59015383, -0.58983928,\n",
       "        -0.5880255 , -0.58757953, -0.59209907, -0.5934035 , -0.58998621,\n",
       "        -0.58854503, -0.58750853, -0.58665922, -0.59111562, -0.58827483,\n",
       "        -0.5876489 , -0.58705365, -0.58613143, -0.58693314]),\n",
       " 'split4_train_score': array([-0.50506011, -0.50095349, -0.5009322 , -0.49913216, -0.49782515,\n",
       "        -0.49867767, -0.50084307, -0.49594107, -0.49642684, -0.49716149,\n",
       "        -0.49767019, -0.49783464, -0.49821959, -0.49509829, -0.49338227,\n",
       "        -0.49302073, -0.49283909, -0.49546327, -0.49650471, -0.49408243,\n",
       "        -0.4927427 , -0.49054341, -0.49107636, -0.49297203]),\n",
       " 'std_fit_time': array([ 63.86990029,  18.34359086,  17.73122123,  12.95327542,\n",
       "         10.70724128,  12.46115336,  16.2440939 ,  36.01119061,\n",
       "         12.67823814,  14.2370642 ,  19.47948781,  22.94037644,\n",
       "         20.73901832,  28.29300658,  28.1789608 ,  22.44711639,\n",
       "         19.54859331,  27.20629722,  28.49641739,  18.52085935,\n",
       "         26.81353981,  12.27796784,  12.39684216, 202.35574139]),\n",
       " 'std_score_time': array([ 2.15511084,  1.1040186 ,  1.65734511,  2.88255323,  4.42803374,\n",
       "         1.20511338,  0.86938426,  5.74373342,  6.96526449,  1.43376494,\n",
       "         1.3485325 ,  5.4354033 ,  7.09768974,  5.39208879,  3.32619293,\n",
       "         1.17372803,  5.67323257,  3.68825518, 13.59570214,  4.58320934,\n",
       "         4.62819582,  4.2454753 ,  5.39182383,  3.01879661]),\n",
       " 'std_test_score': array([0.0035899 , 0.00431981, 0.00322571, 0.00335592, 0.00346963,\n",
       "        0.00299075, 0.00376521, 0.00433453, 0.00415769, 0.0042096 ,\n",
       "        0.00420225, 0.00417192, 0.00424528, 0.0048278 , 0.00397111,\n",
       "        0.00361185, 0.00350549, 0.00336908, 0.00462564, 0.00385183,\n",
       "        0.00330609, 0.00386879, 0.0036061 , 0.00391529]),\n",
       " 'std_train_score': array([0.00051195, 0.0011821 , 0.00090116, 0.00128192, 0.00075573,\n",
       "        0.00089584, 0.0008397 , 0.00155112, 0.00081507, 0.00074208,\n",
       "        0.00043746, 0.00022566, 0.00107651, 0.00108437, 0.00058879,\n",
       "        0.00073995, 0.00100643, 0.00094782, 0.00117498, 0.00099257,\n",
       "        0.00130599, 0.00138444, 0.00066124, 0.00077533])}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch4_1.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.582429 using {'subsample': 0.8, 'colsample_bytree': 0.9}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch4_1.best_score_, gsearch4_1.best_params_))\n",
    "test_means = gsearch4_1.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch4_1.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch4_1.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch4_1.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch4_1.cv_results_).to_csv('my_preds_subsampleh_colsample_bytree_1.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(colsample_bytree), len(subsample))\n",
    "train_scores = np.array(train_means).reshape(len(colsample_bytree), len(subsample))\n",
    "\n",
    "for i, value in enumerate(colsample_bytree):\n",
    "    pyplot.plot(subsample, test_scores[i], label= 'test_colsample_bytree:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'subsample' )                                                                                                      \n",
    "pyplot.ylabel( '-Log Loss' )\n",
    "pyplot.savefig( 'subsample_vs_colsample_bytree1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以得到的两个最优采样率分别为0.8，0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
